论文标题
卷积编码器/解码器网络中的多重数据融合
Multifidelity data fusion in convolutional encoder/decoder networks
论文作者
论文摘要
我们分析了从编码器，解码器和跳过连接组装的卷积神经网络的回归准确性，并通过多倍数数据进行了培训。除了需要训练参数明显少于等效的完全连接的网络，编码器，解码器，编码器解码器或解码器编码器体系结构还可以学习到任意维度输出的输入之间的映射。我们证明了它们的准确性，当对一些高保真性和许多低保真数据进行培训，这些数据从一维函数到二维的泊松方程求解器的模型产生。最终，我们讨论了许多实施选择，以提高蒙特卡洛跌倒块产生的不确定性估计的可靠性，并比较低，高和多差方法之间的不确定性估计。
We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring significantly less trainable parameters than equivalent fully connected networks, encoder, decoder, encoder-decoder or decoder-encoder architectures can learn the mapping between inputs to outputs of arbitrary dimensionality. We demonstrate their accuracy when trained on a few high-fidelity and many low-fidelity data generated from models ranging from one-dimensional functions to Poisson equation solvers in two-dimensions. We finally discuss a number of implementation choices that improve the reliability of the uncertainty estimates generated by Monte Carlo DropBlocks, and compare uncertainty estimates among low-, high- and multifidelity approaches.
